Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	128,675
2	South Dakota	122,999
3	Rhode Island	110,718
4	Utah	109,568
5	Arizona	106,287
6	Tennessee	105,765
7	Wisconsin	102,616
8	Iowa	102,443
9	Arkansas	100,370
10	Oklahoma	100,345
11	Nebraska	99,625
12	Kansas	96,780
13	Alabama	95,412
14	Indiana	94,594
15	Mississippi	93,588
16	Idaho	92,160
17	Nevada	91,486
18	Wyoming	90,656
19	Illinois	89,990
20	Montana	89,065
21	South Carolina	88,153
22	Louisiana	87,978
23	California	85,575
24	Georgia	85,185
25	Texas	84,988
26	Kentucky	84,126
27	New Mexico	84,036
28	Minnesota	82,495
29	Florida	81,587
30	Delaware	81,359
31	Missouri	81,348
32	New Jersey	79,833
33	Ohio	77,922
34	Massachusetts	77,464
35	North Carolina	74,988
36	Alaska	74,678
37	New York	74,596
38	Connecticut	72,347
39	Colorado	70,052
40	West Virginia	68,657
41	Pennsylvania	67,392
42	Michigan	61,824
43	Virginia	60,499
44	Maryland	59,416
45	District of Columbia	52,943
46	New Hampshire	49,361
47	Washington	42,277
48	Puerto Rico	40,266
49	Oregon	34,454
50	Maine	30,154
51	Vermont	20,037
52	Hawaii	18,434

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Carolina	734
2	Arkansas	707
3	Texas	572
4	Kentucky	539
5	Louisiana	530
6	Oklahoma	522
7	South Carolina	515
8	Arizona	511
9	Georgia	501
10	Alabama	473
11	Utah	422
12	Rhode Island	420
13	Florida	391
14	New York	378
15	Connecticut	372
16	Massachusetts	362
17	Virginia	342
18	California	338
19	Ohio	335
20	Tennessee	334
21	New Jersey	328
22	Montana	325
23	Iowa	320
24	Mississippi	316
25	West Virginia	301
26	Pennsylvania	282
27	Nevada	276
28	Kansas	270
29	New Mexico	264
30	Indiana	263
31	Idaho	262
32	Wisconsin	260
33	New Hampshire	259
34	Nebraska	257
35	Delaware	249
36	Alaska	246
37	Maine	245
38	Illinois	240
39	Missouri	239
40	Wyoming	236
41	Colorado	233
42	Vermont	224
43	Michigan	207
44	Maryland	187
45	South Dakota	187
46	Washington	185
47	District of Columbia	168
48	Minnesota	156
49	Oregon	154
50	North Dakota	153
51	Puerto Rico	77
52	Hawaii	54

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,453
2	New York	2,253
3	Massachusetts	2,144
4	Rhode Island	2,085
5	Mississippi	2,077
6	South Dakota	2,021
7	Connecticut	2,015
8	Louisiana	1,945
9	North Dakota	1,898
10	Arizona	1,894
11	Pennsylvania	1,729
12	Indiana	1,728
13	Alabama	1,706
14	Illinois	1,696
15	Arkansas	1,659
16	New Mexico	1,600
17	Iowa	1,595
18	Michigan	1,573
19	Tennessee	1,479
20	South Carolina	1,454
21	Nevada	1,437
22	Georgia	1,348
23	Kansas	1,336
24	Texas	1,331
25	District of Columbia	1,320
26	Florida	1,268
27	Montana	1,225
28	Missouri	1,224
29	Maryland	1,199
30	Delaware	1,165
31	West Virginia	1,160
32	Wisconsin	1,124
33	Minnesota	1,120
34	California	1,089
35	Wyoming	1,078
36	Nebraska	1,060
37	Colorado	1,002
38	Ohio	984
39	Idaho	978
40	North Carolina	931
41	Oklahoma	930
42	Kentucky	921
43	New Hampshire	797
44	Virginia	779
45	Washington	589
46	Puerto Rico	582
47	Utah	533
48	Oregon	478
49	Maine	468
50	Alaska	367
51	Hawaii	291
52	Vermont	290

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Indiana	81
2	Alabama	46
3	Arizona	30
4	Montana	23
5	Tennessee	21
6	Missouri	18
7	Wyoming	16
8	Georgia	15
9	Nevada	15
10	California	14
11	Mississippi	14
12	Iowa	13
13	South Carolina	13
14	Texas	13
15	Arkansas	12
16	Delaware	11
17	North Carolina	11
18	Rhode Island	11
19	New Jersey	10
20	Pennsylvania	10
21	Kansas	9
22	Kentucky	9
23	Louisiana	9
24	New Mexico	9
25	Oklahoma	9
26	West Virginia	9
27	Florida	8
28	Maine	8
29	Massachusetts	8
30	New York	8
31	Alaska	7
32	District of Columbia	7
33	Ohio	7
34	Connecticut	6
35	Michigan	6
36	Nebraska	6
37	New Hampshire	6
38	Virginia	6
39	Wisconsin	6
40	Illinois	5
41	Maryland	5
42	Utah	4
43	Oregon	3
44	South Dakota	3
45	Vermont	3
46	Washington	3
47	Colorado	2
48	Idaho	2
49	Minnesota	2
50	Puerto Rico	2
51	Hawaii	1
52	North Dakota	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	299,950	1	99
Chattahoochee	Georgia	253,965	2	99
Bent	Colorado	238,121	3	99
Dewey	South Dakota	235,743	4	99
Lincoln	Arkansas	235,719	5	99
Davidson	Tennessee	123,113	231	92
Richland	South Carolina	87,883	1351	57
York	South Carolina	81,038	1701	45
Orange	California	78,470	1820	42
Pierce	Washington	39,468	2899	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Buffalo	South Dakota	6,626	3	99
Dickey	North Dakota	6,568	4	99
Iron	Wisconsin	6,506	5	99
Davidson	Tennessee	1,111	1895	39
Richland	South Carolina	1,075	1941	38
Orange	California	1,023	2021	35
York	South Carolina	950	2113	32
Pierce	Washington	532	2661	15

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.